Returns the list of subjects and unit counts covered by Quantabble. Use this to check whether a student's topic is within scope before calling diagnose_response.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits. It states the output is a list (subjects and unit counts) but does not explicitly confirm it is a read-only, side-effect-free operation. While likely safe, best practice would be to state it's non-destructive. Lacks explicit behavioral disclosure.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences: first states what it returns, second gives usage guidance. Front-loaded with primary purpose, no extraneous information. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple, parameterless tool with no output schema, the description is fully sufficient. It covers what the tool returns, its purpose, and its relationship to the sibling tool. No gaps identified given the complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Tool has no parameters and schema description coverage is 100%, so baseline is 4. The description does not need to explain parameters, but it adds no extra detail beyond what the schema already provides. Adequate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it returns a list of subjects and unit counts, specifying the resource (Quantabble's coverage). It distinguishes from sibling tool 'diagnose_response' by explicitly linking its use as a prerequisite check, making the purpose and scope unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Directly states when to use the tool: 'Use this to check whether a student's topic is within scope before calling diagnose_response.' This provides clear context and an explicit alternative, guiding the agent on proper invocation order.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
get_coverage is purely a scope-checking metadata tool, while diagnose_response performs the core diagnostic workflow. Their purposes are clearly separated, so there is no realistic risk of an agent choosing the wrong one.
Both tools follow the same verb_noun snake_case pattern: diagnose_response and get_coverage. The names accurately describe their actions and are fully consistent with each other.
Two tools is on the thin side for a server, though both tools serve clear and necessary roles for a narrow diagnostics use case. The count is borderline rather than fully well-scoped.
The core workflow is covered: check coverage, diagnose a written explanation, and receive both a tutor response and follow-up probe. Minor gaps exist, such as no support for multiple-choice diagnosis or misconception reference details, but agents can work around these.